





"""simple docstring"""





import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		np.ndarray     ):







      '''simple docstring'''




      return input_array.reshape((input_array.size, 1)     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      lowerCAmelCase    		=			np.nan
      for i in range(SCREAMING_SNAKE_CASE     ):
            lowerCAmelCase    		=			features[:, labels == i]
            lowerCAmelCase    		=			data.mean(1     )
            # Centralize the data of class i
            lowerCAmelCase    		=			data - column_reshape(SCREAMING_SNAKE_CASE     )
            if i > 0:
                  # If covariance_sum is not None
                  covariance_sum += np.dot(SCREAMING_SNAKE_CASE				,		centered_data.T     )
            else:
                  # If covariance_sum is np.nan (i.e. first loop)
                  lowerCAmelCase    		=			np.dot(SCREAMING_SNAKE_CASE				,		centered_data.T     )

      return covariance_sum / features.shape[1]


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      lowerCAmelCase    		=			features.mean(1     )
      lowerCAmelCase    		=			np.nan
      for i in range(SCREAMING_SNAKE_CASE     ):
            lowerCAmelCase    		=			features[:, labels == i]
            lowerCAmelCase    		=			data.shape[1]
            lowerCAmelCase    		=			data.mean(1     )
            if i > 0:
                  # If covariance_sum is not None
                  covariance_sum += device_data * np.dot(
                      column_reshape(SCREAMING_SNAKE_CASE     ) - column_reshape(SCREAMING_SNAKE_CASE     )				,		(column_reshape(SCREAMING_SNAKE_CASE     ) - column_reshape(SCREAMING_SNAKE_CASE     )).T				,		)
            else:
                  # If covariance_sum is np.nan (i.e. first loop)
                  lowerCAmelCase    		=			device_data * np.dot(
                      column_reshape(SCREAMING_SNAKE_CASE     ) - column_reshape(SCREAMING_SNAKE_CASE     )				,		(column_reshape(SCREAMING_SNAKE_CASE     ) - column_reshape(SCREAMING_SNAKE_CASE     )).T				,		)

      return covariance_sum / features.shape[1]


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      if features.any():
            lowerCAmelCase    		=			features.mean(1     )
            # Center the dataset
            lowerCAmelCase    		=			features - np.reshape(SCREAMING_SNAKE_CASE				,		(data_mean.size, 1)     )
            lowerCAmelCase    		=			np.dot(SCREAMING_SNAKE_CASE				,		centered_data.T     ) / features.shape[1]
            lowerCAmelCase		,						lowerCAmelCase    		=			np.linalg.eigh(SCREAMING_SNAKE_CASE     )
            # Take all the columns in the reverse order (-1), and then takes only the first
            lowerCAmelCase    		=			eigenvectors[:, ::-1][:, 0:dimensions]
            # Project the database on the new space
            lowerCAmelCase    		=			np.dot(filtered_eigenvectors.T				,		SCREAMING_SNAKE_CASE     )
            logging.info("""Principal Component Analysis computed"""     )

            return projected_data
      else:
            logging.basicConfig(level=logging.ERROR				,		format="""%(message)s"""				,		force=SCREAMING_SNAKE_CASE     )
            logging.error("""Dataset empty"""     )
            raise AssertionError


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		int				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      assert classes > dimensions

      # Check if features have been already loaded
      if features.any:
            lowerCAmelCase		,						lowerCAmelCase    		=			eigh(
                covariance_between_classes(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		covariance_within_classes(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		)
            lowerCAmelCase    		=			eigenvectors[:, ::-1][:, :dimensions]
            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			np.linalg.svd(SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			svd_matrix[:, 0:dimensions]
            lowerCAmelCase    		=			np.dot(filtered_svd_matrix.T				,		SCREAMING_SNAKE_CASE     )
            logging.info("""Linear Discriminant Analysis computed"""     )

            return projected_data
      else:
            logging.basicConfig(level=logging.ERROR				,		format="""%(message)s"""				,		force=SCREAMING_SNAKE_CASE     )
            logging.error("""Dataset empty"""     )
            raise AssertionError


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
      lowerCAmelCase    		=			np.array([0, 0, 0, 1, 1]     )
      lowerCAmelCase    		=			2
      lowerCAmelCase    		=			2

      # Assert that the function raises an AssertionError if dimensions > classes
      with pytest.raises(SCREAMING_SNAKE_CASE     ) as error_info:
            lowerCAmelCase    		=			linear_discriminant_analysis(
                SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
            if isinstance(SCREAMING_SNAKE_CASE				,		np.ndarray     ):
                  raise AssertionError(
                      """Did not raise AssertionError for dimensions > classes"""     )
            assert error_info.type is AssertionError


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
      lowerCAmelCase    		=			2
      lowerCAmelCase    		=			np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]     )

      with pytest.raises(SCREAMING_SNAKE_CASE     ) as error_info:
            lowerCAmelCase    		=			principal_component_analysis(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
            if not np.allclose(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ):
                  raise AssertionError
            assert error_info.type is AssertionError


if __name__ == "__main__":
       import doctest

       doctest.testmod()













"""simple docstring"""





import logging
import os

from .state import PartialState

class 						lowercase   (   logging.LoggerAdapter   ):





      @staticmethod
      def        _snake_case     (			lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			PartialState()
            return not main_process_only or (main_process_only and state.is_main_process)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		*lowercase ,		**lowercase					)						->	Dict:
            if PartialState._shared_state == {}:
                  raise RuntimeError(
                      """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""					)
            lowerCAmelCase    		=			kwargs.pop("""main_process_only""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""in_order""" ,		lowercase					)

            if self.isEnabledFor(lowercase					):
                  if self._should_log(lowercase					):
                        lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                        self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)

                  elif in_order:
                        lowerCAmelCase    		=			PartialState()
                        for i in range(state.num_processes					):
                              if i == state.process_index:
                                    lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                                    self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)
                              state.wait_for_everyone()







def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str = None     ):







      '''simple docstring'''




      if log_level is None:
            lowerCAmelCase    		=			os.environ.get("""ACCELERATE_LOG_LEVEL"""				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			logging.getLogger(SCREAMING_SNAKE_CASE     )
      if log_level is not None:
            logger.setLevel(log_level.upper()     )
            logger.root.setLevel(log_level.upper()     )
      return MultiProcessAdapter(SCREAMING_SNAKE_CASE				,		{}     )






